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The former type, which consists of microphone and Bluetooth devices, helps to obtain contextual information about the user's environments and would be appropriate to perform a deeper TAK875 analysis of the activity, for instance if the user is walking in a disco or at home, if the user is alone or with someone. However, high-level activity recognition (walking, playing, running or standing up) is done using other sensors. ECG can help in determining high-level activities by means of heart rate processing. In this sense, some activities (walking or running) could be discerned based on the effort needed to perform them. The problem here is that ECG sensors are expensive and uncomfortable for the user. In other works [21], data for activity recognition are obtained through any kind of mobile device (not only mobile phones), although these data are sent to a server, where the information is subsequently processed. Thus, the computational cost is not a handicap, as learning and/or recognition are performed in the server and a more complex processing can be applied. In contrast, when processing is carried out in the mobile device itself [30], efficiency becomes a crucial issue. In this vein, in order to apply a solution based on distributed computing, the device must always be connected to a data network. This does not currently represent a major drawback, since most devices have this kind of connectivity, although there are still users (mostly elderly) whose devices have not been associated with a continuous data connection outside the range of WiFi networks. Selleck Nutlin-3 Finally, decrease the energy cost conflicts with the need to send the data collected in a continuous way between device and server. This means that current strategies of sensor batching (as will be seen hereafter) cannot be applied, and devices must be continually waking up from sleep mode. Furthermore, the intensive use of the data network has a deep impact on the energy use. The work in [31] shows the increase in energy consumption when 3G and WiFi are used, and in [32], it can also be observed that approximately 44% of battery usage in smartphones occurs by the use of GSM (3G or 2G). Taking into account previous works, physical activity monitoring through smartphones presents the following challenges: To decrease as far as possible the risk of forgetting the processing device, Adenylyl cyclase so as to carry out continuous monitoring of users, everywhere and anytime; To reduce the energy impact on the smartphone, developing an accurate and efficient system; To integrate learning and monitoring on the device itself, in real time and without server information sharing. Along this work, all of these challenges are addressed, and certain solutions are offered to achieve the proposed objective: to build a complete, accurate and low energy consumption system for pervasive physical activity monitoring using sensors embedded on smartphones.
